Topic
Data management
About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.
Papers published on a yearly basis
Papers
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20 May 2013TL;DR: The paper proposes the SDI generic architecture model that provides a basis for building interoperable data or project centric SDI using modern technologies and best practices and introduces the Scientific Data Lifecycle Management (SDLM) model.
Abstract: Big Data are becoming a new technology focus both in science and in industry. This paper discusses the challenges that are imposed by Big Data on the modern and future Scientific Data Infrastructure (SDI). The paper discusses a nature and definition of Big Data that include such features as Volume, Velocity, Variety, Value and Veracity. The paper refers to different scientific communities to define requirements on data management, access control and security. The paper introduces the Scientific Data Lifecycle Management (SDLM) model that includes all the major stages and reflects specifics in data management in modern e-Science. The paper proposes the SDI generic architecture model that provides a basis for building interoperable data or project centric SDI using modern technologies and best practices. The paper explains how the proposed models SDLM and SDI can be naturally implemented using modern cloud based infrastructure services provisioning model and suggests the major infrastructure components for Big Data.
412 citations
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07 Jun 1999407 citations
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29 Jun 2009TL;DR: This paper will question some of the fundamentals of the OLAP and OLTP separation and present a new proposal for an enterprise data management concept that will allow for revolutionize transactional applications while providing an optimal platform for analytical data processing.
Abstract: When SQL and the relational data model were introduced 25 years ago as a general data management concept, enterprise software migrated quickly to this new technology. It is fair to say that SQL and the various implementations of RDBMSs became the backbone of enterprise systems. In those days. we believed that business planning, transaction processing and analytics should reside in one single system. Despite the incredible improvements in computer hardware, high-speed networks, display devices and the associated software, speed and flexibility remained an issue. The nature of RDBMSs, being organized along rows, prohibited us from providing instant analytical insight and finally led to the introduction of so-called data warehouses. This paper will question some of the fundamentals of the OLAP and OLTP separation. Based on the analysis of real customer environments and experience in some prototype implementations, a new proposal for an enterprise data management concept will be presented. In our proposal, the participants in enterprise applications, customers, orders, accounting documents, products, employees etc. will be modeled as objects and also stored and maintained as such. Despite that, the vast majority of business functions will operate on an in memory representation of their objects. Using the relational algebra and a column-based organization of data storage will allow us to revolutionize transactional applications while providing an optimal platform for analytical data processing. The unification of OLTP and OLAP workloads on a shared architecture and the reintegration of planning activities promise significant gains in application development while simplifying enterprise systems drastically. The latest trends in computer technology -- e.g. blade architecture, multiple CPUs per blade with multiple cores per CPU allow for a significant parallelization of application processes. The organization of data in columns supports the parallel use of cores for filtering and aggregation. Elements of application logic can be implemented as highly efficient stored procedures operating on columns. The vast increase in main memory combined with improvements in L1--, L2--, L3--caching, together with the high data compression rate column storage will allow us to support substantial data volumes on one single blade. Distributing data across multiple blades using a shared nothing approach provides further scalability.
404 citations
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01 Jan 1984
TL;DR: In this paper, the authors present a management and the environment: managers and the evolution of management managers and their environments managing in a global environment social and ethical responsibilities of management, and work and organizations: management decision-making the planning function strategic planning the organizing function organization design the controlling function.
Abstract: Part 1 Management and the environment: managers and the evolution of management managers and their environments managing in a global environment social and ethical responsibilities of management. Part 2 Managing work and organizations: management decision-making the planning function strategic planning the organizing function organization design the controlling function. Part 3 Managing people in organizations: motivation managing work groups leading people in organizations communication and negotiation human resource management organization change, development and innovation. Part 4 Managing production and operations: production and operations management production and inventory planning and control managing information for decision making. Part 5 Special management topics: entrepreneurship careers in management. Appendix: Internet exercises.
402 citations
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20 May 2003TL;DR: Piazza offers a language for mediating between data sources on the Semantic Web, which maps both the domain structure and document structure and enables interoperation of XML data with RDF data that is accompanied by rich OWL ontologies.
Abstract: The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data model. To flourish, the Semantic Web needs to be able to accommodate the huge amounts of existing data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world's data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies.This paper describes the Piazza system, which addresses these challenges. Piazza offers a language for mediating between data sources on the Semantic Web, which maps both the domain structure and document structure. Piazza also enables interoperation of XML data with RDF data that is accompanied by rich OWL ontologies. Mappings in Piazza are provided at a local scale between small sets of nodes, and our query answering algorithm is able to chain sets mappings together to obtain relevant data from across the Piazza network. We also describe an implemented scenario in Piazza and the lessons we learned from it.
402 citations